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Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (GRNs). Here we present a method for the inference of enhancer-driven GRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TFs) and links these enhancers to candidate target genes. To improve both recall and precision of TF identification, we curated and clustered a motif collection with more than 30,000 motifs. We benchmarked SCENIC+ on diverse datasets from different species, including human peripheral blood mononuclear cells, ENCODE cell lines, melanoma cell states and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers and GRNs between human and mouse cell types in the cerebral cortex. Finally, we use SCENIC+ to study the dynamics of gene regulation along differentiation trajectories and the effect of TF perturbations on cell state. SCENIC+ is available at scenicplus.readthedocs.io . SCENIC+ is a comprehensive toolbox for inferring and analyzing enhancer-driven gene regulatory networks using single-cell multiomic data.

Background Multiplexing of samples in single-cell RNA-seq studies allows a significant reduction of the experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or -lipids allow barcoding sample-specific cells, a process called “hashing.” Results Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. We also compare TotalSeq-B antibodies with CellPlex reagents (10x Genomics) on human PBMCs and TotalSeq-B with different lipids on primary mouse tissues. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines and mouse strains. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects. Conclusions Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. On nuclei datasets, lipid hashing delivers the best results. Lipid hashing also outperforms antibodies on cells isolated from mouse brain. However, antibodies demonstrate better results on tissues like spleen or lung.

It is commonly believed that trees were absent in Scandinavia during the last glaciation and first recolonized the Scandinavian Peninsula with the retreat of its ice sheet some 9000 years ago. Here, we show the presence of a rare mitochondrial DNA haplotype of spruce that appears unique to Scandinavia and with its highest frequency to the west—an area believed to sustain ice-free refugia during most of the last ice age. We further show the survival of DNA from this haplotype in lake sediments and pollen of Trondelag in central Norway dating back ~10,300 years and ch loro plast DNA of pine and spruce in lake sediments adjacent to the ice-free Andeya refugium in northwestern Norway as early as ~22,000 and 17,700 years ago, respectively. Our findings imply that conifer trees survived in ice-free refugia of Scandinavia during the last glaciation, challenging current views on survival and spread of trees as a response to climate changes.

We compared DNA, pollen and macrofossil data obtained from Weichselian interstadial (age more than 40 kyr) and Holocene (maximum age 8400 cal yr BP) peat sediments from northern Europe and used them to reconstruct contemporary floristic compositions at two sites. The majority of the samples provided plant DNA sequences of good quality with success amplification rates depending on age. DNA and sequencing analysis provided five plant taxa from the older site and nine taxa from the younger site, corresponding to 7% and 15% of the total number of taxa identified by the three proxies together. At both sites, pollen analysis detected the largest (54) and DNA the lowest (10) number of taxa, but five of the DNA taxa were not detected by pollen and macrofossils. The finding of a larger overlap between DNA and pollen than between DNA and macrofossils proxies seems to go against our previous suggestion based on lacustrine sediments that DNA originates principally from plant tissues and less from pollen. At both sites, we also detected Quercus spp. DNA, but few pollen grains were found in the record, and these are normally interpreted as long-distance dispersal. We confirm that in palaeoecological investigations, sedimentary DNA analysis is less comprehensive than classical morphological analysis, but is a complementary and important tool to obtain a more complete picture of past flora.

In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within the liver lobule. However, it is unclear whether this spatial variation, called zonation, is governed by a well-defined gene regulatory code. Here, using a combination of single-cell multiomics, spatial omics, massively parallel reporter assays and deep learning, we mapped enhancer-gene regulatory networks across mouse liver cell types. We found that zonation affects gene expression and chromatin accessibility in hepatocytes, among other cell types. These states are driven by the repressors TCF7L1 and TBX3, alongside other core hepatocyte transcription factors, such as HNF4A, CEBPA, FOXA1 and ONECUT1. To examine the architecture of the enhancers driving these cell states, we trained a hierarchical deep learning model called DeepLiver. Our study provides a multimodal understanding of the regulatory code underlying hepatocyte identity and their zonation state that can be used to engineer enhancers with specific activity levels and zonation patterns. Bravo González-Blas et al. uncover enhancer-gene regulatory networks underlying hepatocyte identity and their zonation state by combining single-cell and spatial multiomics with massively parallel reporter assays and deep learning.

Background For their transmission, African trypanosomes rely on their blood feeding insect vector, the tsetse fly ( Glossina sp.). The ingested Trypanosoma brucei parasites have to overcome a series of barriers in the tsetse fly alimentary tract to finally develop into the infective metacyclic forms in the salivary glands that are transmitted to a mammalian host by the tsetse bite. The parasite population in the salivary gland is dense with a significant number of trypanosomes tightly attached to the epithelial cells. Our current knowledge on the impact of the infection on the salivary gland functioning is very limited. Therefore, this study aimed to gain a deeper insight into the global gene expression changes in the salivary glands of Glossina morsitans morsitans in response to an infection with the T. brucei parasite. A detailed whole transcriptome comparison of midgut-infected tsetse with and without a mature salivary gland infection was performed to study the impact of a trypanosome infection on different aspects of the salivary gland functioning and the mechanisms that are induced in this tissue to tolerate the infection i.e. to control the negative impact of the parasite presence. Moreover, a transcriptome comparison with age-matched uninfected flies was done to see whether gene expression in the salivary glands is already affected by a trypanosome infection in the tsetse midgut. Results By a RNA-sequencing (RNA-seq) approach we compared the whole transcriptomes of flies with a T. brucei salivary gland/midgut infection versus flies with only a midgut infection or versus non-infected flies, all with the same age and feeding history. More than 7500 salivary gland transcripts were detected from which a core group of 1214 differentially expressed genes (768 up- and 446 down-regulated) were shared between the two transcriptional comparisons. Gene Ontology enrichment analysis and detailed gene expression comparisons showed a diverse impact at the gene transcript level. Increased expression was observed for transcripts encoding for proteins involved in immunity (like several genes of the Imd-signaling pathway, serine proteases, serpins and thioester-containing proteins), detoxification of reactive species, cell death, cytoskeleton organization, cell junction and repair. Decreased expression was observed for transcripts encoding the major secreted proteins such as 5′-nucleotidases, adenosine deaminases and the nucleic acid binding proteins Tsals. Moreover, expression of some gene categories in the salivary glands were found to be already affected by a trypanosome midgut infection, before the parasite reaches the salivary glands. Conclusions This study reveals that the T. brucei population in the tsetse salivary gland has a negative impact on its functioning and on the integrity of the gland epithelium. Our RNA-seq data suggest induction of a strong local tissue response in order to control the epithelial cell damage, the ROS intoxication of the cellular environment and the parasite infection, resulting in the fly tolerance to the infection. The modified expression of some gene categories in the tsetse salivary glands by a trypanosome infection at the midgut level indicate a putative anticipatory response in the salivary glands, before the parasite reaches this tissue.

Background Tsetse flies ( Glossina sp.) are the vectors of human and animal trypanosomiasis throughout sub-Saharan Africa. Tsetse flies are distinguished from other Diptera by unique adaptations, including lactation and the birthing of live young (obligate viviparity), a vertebrate blood-specific diet by both sexes, and obligate bacterial symbiosis. This work describes the comparative analysis of six Glossina genomes representing three sub-genera: Morsitans ( G. morsitans morsitans , G. pallidipes , G. austeni ), Palpalis ( G. palpalis , G. fuscipes ), and Fusca ( G. brevipalpis ) which represent different habitats, host preferences, and vectorial capacity. Results Genomic analyses validate established evolutionary relationships and sub-genera. Syntenic analysis of Glossina relative to Drosophila melanogaster shows reduced structural conservation across the sex-linked X chromosome. Sex-linked scaffolds show increased rates of female-specific gene expression and lower evolutionary rates relative to autosome associated genes. Tsetse-specific genes are enriched in protease, odorant-binding, and helicase activities. Lactation-associated genes are conserved across all Glossina species while male seminal proteins are rapidly evolving. Olfactory and gustatory genes are reduced across the genus relative to other insects. Vision-associated Rhodopsin genes show conservation of motion detection/tracking functions and variance in the Rhodopsin detecting colors in the blue wavelength ranges. Conclusions Expanded genomic discoveries reveal the genetics underlying Glossina biology and provide a rich body of knowledge for basic science and disease control. They also provide insight into the evolutionary biology underlying novel adaptations and are relevant to applied aspects of vector control such as trap design and discovery of novel pest and disease control strategies.

We compared DNA, pollen and macrofossil data obtained from Weichselian interstadial (age more than 40 kyr) and Holocene (maximum age 8400 cal yr BP) peat sediments from northern Europe and used them to reconstruct contemporary floristic compositions at two sites. The majority of the samples provided plant DNA sequences of good quality with success amplification rates depending on age. DNA and sequencing analysis provided five plant taxa from the older site and nine taxa from the younger site, corresponding to 7% and 15% of the total number of taxa identified by the three proxies together. At both sites, pollen analysis detected the largest (54) and DNA the lowest (10) number of taxa, but five of the DNA taxa were not detected by pollen and macrofossils. The finding of a larger overlap between DNA and pollen than between DNA and macrofossils proxies seems to go against our previous suggestion based on lacustrine sediments that DNA originates principally from plant tissues and less from pollen. At both sites, we also detected Quercus spp. DNA, but few pollen grains were found in the record, and these are normally interpreted as long-distance dispersal. We confirm that in palaeoecological investigations, sedimentary DNA analysis is less comprehensive than classical morphological analysis, but is a complementary and important tool to obtain a more complete picture of past flora.

Joint profiling of chromatin accessibility and gene expression of individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (eGRN). Here we present a new method for the inference of eGRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TF) and links these enhancers to candidate target genes. Specific TFs for each cell type or cell state are predicted based on the concordance of TF binding site accessibility, TF expression, and target gene expression. To improve both recall and precision of TF identification, we curated and clustered more than 40,000 position weight matrices that we could associate with 1,553 human TFs. We validated and benchmarked each of the SCENIC+ components on diverse data sets from different species, including human peripheral blood mononuclear cell types, ENCODE cell lines, human melanoma cell states, and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers, and GRNs between human and mouse cell types in the cerebral cortex. Finally, we provide new capabilities that exploit the inferred eGRNs to study the dynamics of gene regulation along differentiation trajectories; to map regulatory activities onto tissues using spatial omics data; and to predict the effect of TF perturbations on cell state. SCENIC+ provides critical insight into gene regulation, starting from multiome atlases of scATAC-seq and scRNA-seq. The SCENIC+ suite is available as a set of Python modules at https://scenicplus.readthedocs.io.

Cell type identity is encoded by gene regulatory networks (GRN), in which transcription factors (TFs) bind to enhancers to regulate target gene expression. In the mammalian liver, lineage TFs have been characterized for the main cell types, including hepatocytes. Hepatocytes cover a relatively broad cellular state space, as they differ significantly in their metabolic state, and function, depending on their position with respect to the central or portal vein in a liver lobule. It is unclear whether this spatially defined cellular state space, called zonation, is also governed by a well-defined gene regulatory code. To address this challenge, we have mapped enhancer-GRNs across liver cell types at high resolution, using a combination of single cell multiomics, spatial omics, GRN inference, and deep learning. We found that cell state changes in transcription and chromatin accessibility in hepatocytes, liver sinusoidal endothelial cells and hepatic stellate cells depend on zonation. Enhancer-GRN mapping suggests that zonation states in hepatocytes are driven by the repressors Tcf7l1 and Tbx3, that modulate the core hepatocyte GRN, controlled by Hnf4a, Cebpa, Hnf1a, Onecut1 and Foxa1, among others. To investigate how these TFs cooperate with cell type TFs, we performed an in vivo massively parallel reporter assay on 12,000 hepatocyte enhancers and used these data to train a hierarchical deep learning model (called DeepLiver) that exploits both enhancer accessibility and activity. DeepLiver confirms Cebpa, Onecut, Foxa1, Hnf1a and Hnf4a as drivers of enhancer specificity in hepatocytes; Tcf7l1/2 and Tbx3 as regulators of the zonation state; and Hnf4a, Hnf1a, AP-1 and Ets as activators. Finally, taking advantage of in silico mutagenesis predictions from DeepLiver and enhancer assays, we confirmed that the destruction of Tcf7l1/2 or Tbx3 motifs in zonated enhancers abrogates their zonation bias. Our study provides a multi-modal understanding of the regulatory code underlying hepatocyte identity and their zonation state, that can be exploited to engineer enhancers with specific activity levels and zonation patterns.